通过动量选择性层析成像探索拓扑
Exploring topology via momentum-selective tomography
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中文总结 AI 辅助
提出动量选择性层析成像框架，在超导处理器上直接提取缠绕相位，绘制SSH模型完整拓扑相图，实现量子化缠绕数测量，为拓扑量子计算开辟新途径。
中文摘要 AI 辅助
由于动量本征态在实空间中的非局域性质，识别和测量拓扑不变量仍然是一个基本挑战。基于布洛赫振荡、量子行走、电荷泵浦和猝灭动力学的既有方法通常无法提供每个布洛赫态的直接动量相关几何信息。在此，我们提出了一种通过动量选择性测量实现直接能带拓扑层析成像的新框架，并在具有高度连通耦合架构的超导处理器上进行了实验演示。利用一种“星形”几何结构，其中辅助探针与具有空间调制耦合强度的一维拓扑链耦合，我们选择性地寻址该链的期望动量态。该方法能够直接且稳健地提取有助于相关拓扑不变量的缠绕相位。利用由长钽空气桥桥接的非局域耦合器，我们实现了扩展的Su-Schrieffer-Heeger（SSH）模型，并绘制了其完整的拓扑相图。通过对时间和空间进行嵌套傅里叶分析，我们直接提取了布里渊区上的缠绕相位，并解析了由相位缠绕{0, ±2π, 4π}表征的量子化缠绕数ν={0,±1,2}。我们的工作建立了一种探测拓扑特性的通用工具，并为基于拓扑保护的稳健量子计算架构开辟了新途径。
英文摘要
Identifying and measuring topological invariants remains a fundamental challenge due to the non-local nature of momentum eigenstates in real space. Established approaches based on Bloch oscillations, quantum walks, charge pumping and quench dynamics, typically fail to provide direct momentum-dependent geometric information of each Bloch state. Here, we propose a novel framework for implementing direct band-topology tomography via momentum-selective measurements, and experimentally demonstrate it on a superconducting processor featuring a highly connected coupling architecture. Utilizing a ``star-like" geometry in which an auxiliary probe couples to a one-dimensional topological chain with spatially modulated coupling strengths, we selectively address the desired momentum states of the chain. This approach enables the direct and robust extraction of the winding phase that contributes to the associated topological invariant. Leveraging non-local couplers bridged by long tantalum airbridges, we realize an extended Su-Schrieffer-Heeger (SSH) model and map its full topological phase diagram. Through a nested Fourier analysis over time and space, we directly extract the winding phase across Brillouin zone and resolve quantized winding numbers of $ν=\{0,\pm1,2\}$ characterized by phase winding $\{0, \pm 2π,4π\}$. Our work establishes a versatile tool for probing topological characteristics and opens a new avenue for robust quantum computing architectures based on topological protection.
发表机构
- Tencent（腾讯）
- Shenzhen University（深圳大学）
机构由 AI 辅助整理，请以论文原文为准。
